Reanalysis and Reproduce of Long-term Hydrologic Data using Limited Observation

2016 Fiscal Year Final Research Report

• Project/Area Number: 26630226
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Hydraulic engineering
• Research Institution: Kyoto University
• Principal Investigator: Kim Sunmin 京都大学, 工学研究科, 准教授 (10546013)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 水文データ / 再解析

Outline of Final Research Achievements

This study tested various hydrologic model such as, Kinematic Wave model, Tank model, Artificial Neural Network model (ANN), to reproduce and reanalysis the long-term hydrologic data, especially for river discharge data. Among those tested hydrological models, ANN model provides plausible results with its modeling flexibility and estimation performance. ANN model allows us to model by linking any variables that are related without physical connectivity and physical concept behind.
First application was on the long-term dam inflow estimation for Naramata Dam reservoir at the upper basin of the Tone river. Second application was on water stage estimation by utilizing the water stage information from neighboring water gauge stations. Both results provide prominent results to reproduce hydrologic data for long-term data reanalysis.

Free Research Field

水工学